The present invention relates to the general technical area of modeling interactions between various entities, such as a customer and a telephone call center. More specifically, it relates to modeling interactions to determine expected values (e.g., probability values) for reaching an end event in the presence of uncertainty.
Consumers of products and services are increasingly using automated interaction channels such as Internet web sites and telephone call centers. Such automated sales channels typically provide an automated process which attempts to match potential customers with desirable products and/or services. In the case of web sites, the interaction channel may be fully automated. In the case of call centers, human customer-service agents are often used. One goal of the companies selling the products and services is to maximize total enterprise profitability and, therefore, companies will often invest heavily in creating computerized models in an attempt to maximize their revenue and minimize their expenses for both of these types of sales channels.
Prediction modeling is generally used to predict the outcome of numerous decisions which could be implemented. In a most simplistic example, a prediction model may predict the likelihood (or probability) of a particular result or outcome occurring if a particular action was performed (e.g., a particular decision is carried out) under one or more specific conditions. In a more complex scenario, a prediction model may predict the probabilities of a plurality of outcomes for a plurality of actions being performed under various conditions.
In a specific application, prediction modeling may be used to decide which specific interactions are to be taken by a company's service or product sales center (e.g., website or telephone call center) when a customer is interacting with such center. The prediction modeling helps the company select an interaction that is likely to result in a desirable goal being met. Automated sales centers, for example, typically provide an automated process which attempts to match potential or current customers with desirable products and/or services. In the case of websites, the sales center may be fully automated. In the case of call centers, human customer-service agents in conjunction with automated interactive voice recognition (IVR) processes or agents are often used.
For example, a customer may go to a particular website or call center of a company which specializes in selling automobiles. From the company's perspective, the company may have a goal of maximizing automobile revenue to each customer who interacts with its website or telephone call center. When a customer initially accesses the website or call center, it may be possible to select any number of sales promotions to present to the customer (e.g., via a web page or communicated by a human sales agent). Prediction models may be used to determine which sale promotion to present to a given customer to more likely achieve the goal of maximizing sales revenue. For instance, it may be determined that a particular type of customer is highly likely to buy a particular type of automobile if presented with a sales presentation for such item.
Typically, customers take actions to move through a series of events which define a particular “pipeline.” For example, a customer may first view a product on a web page, then add the viewed web product to their shopping cart, and finally purchase the product. While companies have realized that their goal or goals (e.g., maximizing revenue) are dependent upon these pipelines, conventional methodologies for determining expected values (such as the probability of a potential customer purchasing an offered product) have been inadequate. Additionally, even when data is collected over time to determine an end goal's expected value, such as the probability of purchasing a product via a particular pipeline, another pipeline end goal's expected value may not have enough data collected to ensure the reliability of a prediction calculation based on such collected data. In other words, prediction methodologies are inadequate in the presence of uncertainty.
In view of the above, there is a need for improved mechanisms for affectively using prediction models to determine the expected value of end goals for a pipeline scenario in the presence of uncertainty.